What does Big Data Analytics, The Cloud, and IIoT Mean for Your Maintenance?

Big Data. The Cloud. The Industrial Internet of Things (IIoT). The next iteration of asset management according to industry experts. These tools will provide you with new and innovative ways to manage assets. Yet with all these perceived benefits, enterprises are reluctant to embrace such technology. These are must-have tools so why is this happening? There are many contributing factors to this slow adoption.

Understanding these factors helps your business make sense of new asset management strategies. It also gives industry vendors and experts important information. How can we do a better job of showcasing these new strategies and technologies we develop?

Recognizing Value

The key to a new trend or technology being adopted by the industry is the ability to recognize the value in it. People talk about this frequently, but what does it actually mean for you and your business? Simply put, they need to provide a better solution than the current one or solve a previously unsolved problem for businesses. Think about predictive maintenance for a moment. At the time, preventive maintenance was being regularly used as a maintenance practice. Work was scheduled at regular intervals to perform maintenance so that assets didn’t break.

But then someone came along and said that maintenance work can go above and beyond by capturing information about the health of the assets. By capturing information using portable analyzers, we can use this information to see trends in behavior, visualize wear and tear, and forecast how long assets can function before needing maintenance.

Organizations were now able to see a clearer picture of asset performance rather than having to guess or estimate. Some found that they were performing maintenance much more often than what was actually required, allowing them to adjust their schedules and save a substantial amount of money. Predictive maintenance was not only able to demonstrate benefits that made sense from a business perspective but was also worth the costs associated with implementing a shift in maintenance strategies for many organizations.

Continued Improvement Through Information

We have always looked to improve maintenance by examining and reviewing how we do it. We identify areas that are lacking and find places where the process breaks down, then search for solutions on how to fix them. Historically, this process has served us well. Great strides have been made in how assets are managed, with reliability and efficiency drastically improving.

Today, the same logic is proposed for taking the next step forward. Using big data analytics, the cloud, and IIoT will allow organizations to find new and improved ways to measure asset performance. Working together, these technologies will unlock access to information hidden in your daily maintenance operations much like predictive maintenance did. However, this data mining will happen on a much larger scale.

IIoT – The Gathering of Data

Physical assets are trying to speak to you whether you realize it or not. Temperatures, vibrations, pressures, power consumption, and other measurements are all direct indications of how healthy an asset is. Problem is, access to this information has been a challenge until now thanks to concepts like the Internet of Things (IoT).

The main objective of IoT is to enable device connectivity. In the consumer marketplace, this idea is often referred to as smart devices. For example, home appliances like washing machines can detect when detergent is low and order more for you automatically or your thermostat can learn your habits and adjust your home’s temperature automatically by detecting your proximity. All of this happens because of this connectivity. In the industrial world (IIoT), this connectivity provides a very different benefit.

For businesses, the real benefit of this connectivity is the ability to capture and examine information about current asset conditions. This is generally accomplished by retrofitting an asset with sensors to read various measurements about asset health. These sensors can be configured to read measurements at pre-defined intervals and save them for later review.

Big Data Analytics – Knowledge is Power

Capturing more information about your assets is a great thing, but making sense of it presents some challenges. First, the data isn’t worth anything if you can’t analyze it in meaningful ways. Modern analytical tools are going above and beyond showing you basic information like the number of open work orders. Rather, big data provides you with ways to dig deep into large amounts of information and then perform complex manipulations to find trends and even simulate “what-if” scenarios.

This second part is extremely important. Using analytics, a company can learn exactly how much is being spent on the maintenance of a specific asset or set of assets and then run test cases to find ways to reduce this cost. Consider this article written by Bernard Marr, an expert on big data and analytics. He highlights how a customer of Caterpillar Marine found that a glaring inefficiency in their operations and were able to remedy it.

Using analytics, the customer was able to determine that their current schedule for ship cleanings was not adequate, causing the ships to be extremely inefficient in how they operated. It was then recommended that the frequency of cleanings be changed from every two years to every 6 months. Even though this substantially inflates the budget for cleanings (from 20,000 dollars to 80,000 dollars), the savings gained from increasing how efficiently the ship could glide through the water eclipsed this by a substantial margin. The analytical data showed that the company could expect to see savings of nearly 5 million dollars annually, just by cleaning their hulls more frequently.

This example highlights how analytics can provide an enterprise with real value. While any savings in your budget may or may not be of this magnitude, it’s important to realize that there is information like this hidden in your maintenance operations. You must have the tools to uncover them, and big data can provide you with them.

Cloud Computing – Shouldering the Load

Capturing data and analyzing it is a great thing but without cheap and effective ways to house and process it, the concept of Big Data isn’t feasible. Traditionally speaking, data warehousing has been cost prohibitive. Large scale data storage and manipulation (terabytes or more in many cases) require substantial disk space and processing power. Housing this kind of hardware also presents additional expenses in the form of power consumption and management resources from IT. Then there’s the scaling issue. Can you guarantee that you’ll always have enough processing power and storage space? As it stood, Big Data wasn’t a realistic concept for many.

Until the cloud showed up, that is. Describe as a lot of different things, the cloud enables businesses to store large amounts of data and process it in a cost effective way. Typical servers involve buying hardware, installing it, then supporting it until the hardware is retired. The servers will also require a large amount of electricity during their lifetime.

Cloud services not only shift this burden away from your business, they also provide you with higher levels of service at a lower cost to you because providers favor usage-based pricing models. Usage based billing allows your business to only pay for what you actually use rather than continuously paying for the management and maintenance of hardware. And unlike servers located internally, cloud storage and computing power are both instantly and almost infinitely scalable, meaning that you never have to worry about limitations for analytics.

The Complete Package & End Goals

Each of these technologies can provide a business with powerful and meaningful ways to decrease costs and improve asset performance. In order to truly realize the various benefits of analytics, IIoT, and cloud computing, you must understand that there isn’t a singular hero when it comes to data insight. Rather, it’s about IIoT, Big Data, and the Cloud working together to enable each other. The data collected by IIoT hardware is not meaningful if analytics cannot be performed. Likewise, Big Data is not feasible if the Cloud isn’t around to provide cost effective processing power and storage. And the Cloud isn’t useful unless there’s a need for storage and computing capabilities.

Even with these benefits, there is still a ways to go before big data, IIoT, and the cloud becomes the status quo. Some businesses may not be willing to implement such a solution right away, while others might still be skeptical of the benefits. Eventually, the process of collecting large amounts of data for analytics will become the baseline of how we measure asset performance. In the end, it’s up to you and your business to determine how to best spend your budgets. Big Data, IIoT, and the Cloud can certainly provide you with an edge, though.